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基于直推式支持向量机的图像分类算法 被引量：10

Image classification based on transductive support vector machines
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摘要 直推式支持向量机(TSVM)是在利用有标签样本的同时,考虑无标签样本对分类器的影响,并且结合支持向量机算法,实现一种高效的分类算法。它在包含少量有标签样本的训练集和大量无标签样本的测试集上,具有良好的效果。但是它有算法时间复杂度比较高,需要预先设置正负例比例等不足。通过对原有算法的改进,新算法在时间复杂度上明显下降,同时算法效果没有明显的影响。 Transductive Support Vector Machines （TSVM） take advantage of the test sets as well as the train sets and inherit most properties of inductive SVMs. They are more efficient than inductive SVMs, especially for very small training sets and large test sets. But they still have disadvantages, such as high time complexity and the requirement of ＂num ＋ ＂. The improved algorithm substantially reduces the time complexity with little influence on the performance.
作者 沈新宇 许宏丽 官腾飞
机构地区 北京交通大学计算机与信息技术学院
出处 《计算机应用》 CSCD 北大核心 2007年第6期1463-1464,1467,共3页 journal of Computer Applications
基金 国家自然科学基金资助项目(K06A30060) 北京交通大学"十五"科技基金资助项目(2004SM013)
关键词 支持向量机 直推式学习 图像分类 Support Vector Machine （SVM） transductive learning image classification
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献5

  • 1VAPNIK V.Statistical Learning Theory[M].New York:Wiley,1998.
  • 2JOACHIMS T.Learning to classify text using support vector machines-methods,theory and algorithms[M].Netherlands:Kluwer,2002.
  • 3JOACHIMS T.SVMlight Support Vector Machine[EB/OL].http://svmlight.Joachims.org,2001.
  • 4JOACHIMS T.Transductive inference for text classification using support machines[A].Proceedings of ICML-99,16th International Conference on Machine Learning[C].San Francisco,US.Morgan Kaufmann Publishers,1999b.200-209.
  • 5陈毅松,汪国平,董士海.基于支持向量机的渐进直推式分类学习算法[J].软件学报,2003,14(3):451-460. 被引量：89

二级参考文献17

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计算机应用
2007年 第6期

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